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Indicated for use by health care professionals whenever there is a need for monitoring the physiological parameters of patients. For monitoring, recording, and alarming of multiple physiological parameters of adults, pediatrics and neonates in healthcare environments. Additionally, the monitors may be used in transport situations within a healthcare facility.

The SureSigns VS2 Vital Signs monitor and the SureSigns VM1 are for use by health care professionals whenever there is a need for monitoring the physiological parameters of patients. For monitoring, recording, and alarming of multiple physiological parameters of adults, pediatrics and neonates in healthcare environments. Additionally, the monitors may be used in transport situations within a healthcare facility. The addition of the wireless functionality to the predicate VS3 does not change the intended use or indications for use. The subject devices have the same fundamental technological characteristics as the legally marketed predicate devices. The subject devices use the same design as the predict device. The composition of the VS2 and the VM1 materials are different than the predicate device, however are made of a material previously used in other medical devices. The chemical composition of the subject devices has changed but is the same as material used in other predicate devices. The energy source of the subject device the VS2 is an external power supply, while the VM1 will use a power supply that is the same as the predicate device. The VS2 and the VM1 both can run on battery power with batteries similar to the predicate devices.

The provided 510(k) summary for the Philips SureSigns VM1 Patient Monitors and SureSigns VS2 Vital Signs Monitor does not contain the detailed information necessary to complete a table of acceptance criteria and reported device performance, nor does it describe a study proving the device meets specific acceptance criteria in the way typically expected for AI/ML device evaluations.

This document is a pre-market notification (510(k)) for physiological monitoring devices, specifically patient monitors, and their modifications. The evaluation for substantial equivalence in such devices often focuses on comparing technical characteristics and performance to legally marketed predicate devices, rather than establishing novel performance metrics against a defined ground truth using a clinical study with a specified sample size and expert review in the context of an algorithm.

Here's a breakdown of what can be extracted and what is missing:

The document states:
"Verification, validation, and testing activities establish the performance, functionality, and reliability characteristics of the subject devices with respect to the predicates. Testing involved system level tests, performance tests, and safety testing from hazard analysis. Pass/Fail criteria were based on the specifications cleared for the predicate device, the specifications of the subject device and test results showed substantial equivalence. The results demonstrate that the Philips SureSigns VM1 Patient Monitors and SureSigns VS2 Vital Signs monitor and the modification to the SureSigns VS3 Vital Signs Monitor meet all reliability requirements and performance claims and supports a determination of substantial equivalence."

This indicates that the "acceptance criteria" were essentially the specifications of the predicate device and the subject device, and the "study" was system-level, performance, and safety testing aimed at demonstrating substantial equivalence. However, specific numerical performance metrics are not provided in this summary.

Therefore, many parts of your request cannot be directly answered from the provided text.

1. Table of acceptance criteria and the reported device performance

2. Sample size used for the test set and the data provenance

• Sample Size: Not specified. The document mentions "system level tests, performance tests, and safety testing," but does not give a sample size of patients or data records.
• Data Provenance: Not specified. It's likely these tests were conducted internally by Philips Medical Systems (Andover, MA, United States), but the origin of any clinical data (if used) is not detailed. The distinction between retrospective or prospective data is not applicable given the type of testing described (device function/safety rather than algorithm performance on clinical data).

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

• Not applicable / Not specified. This type of information (number and qualification of experts establishing ground truth) is typically relevant for studies evaluating an AI/ML diagnostic or prognostic system against a human standard. For a patient monitor, "ground truth" would be established by reference standards or direct physical measurements, not expert consensus on interpretations of images or signals.

4. Adjudication method for the test set

• Not applicable / Not specified. Adjudication methods (like 2+1, 3+1) are used to resolve disagreements among multiple human readers when establishing ground truth. This is not typically part of the testing for a physiological monitor's basic functions.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

• Not applicable / Not done. This is a physiological monitor, not an AI-assisted diagnostic tool for human readers. Therefore, an MRMC study and AI assistance effect size are not relevant to this device's evaluation.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

• Not applicable / Not mentioned in this context. While these devices contain algorithms for processing physiological signals (e.g., arrhythmia detection, NIBP calculation), the 510(k) summary does not describe a "standalone" algorithm performance study in the way it's typically understood for AI/ML devices where the algorithm's output is compared to a ground truth independent of human interaction. The testing described focuses on the integrated device's performance.

7. The type of ground truth used

• Likely reference standards and direct physical measurements. For physiological monitors, ground truth for parameters like blood pressure, heart rate, or SpO2 would typically involve highly accurate reference devices or invasive measurements (e.g., arterial line for blood pressure) against which the monitor's readings are compared. The document does not explicitly state this but it's the standard practice for such devices.
• Not pathology, outcomes data, or expert consensus in the context of diagnostic interpretation.

8. The sample size for the training set

• Not applicable / Not specified. These devices are not described as employing machine learning models that require a "training set" in the modern sense. They rely on established signal processing algorithms. If any adaptive algorithms or calibrations exist, the "training" would be internal calibration processes rather than a distinct dataset.

9. How the ground truth for the training set was established

• Not applicable. As a traditional physiological monitor, the concept of a "training set" and associated ground truth establishment for a machine learning model is not relevant to the information provided in this 510(k) summary.

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